5 Common Issues With Image Processing

EM Admin
Jun 23, 2025
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With lots of innovations taking place around the globe, Google has introduced Google Lens to recognize a place accurately for smooth navigation. This is an incredible breakthrough, ensuring that digital images remove barriers for people who look out for a specific location. Likewise, image processing is emerging as a powerful tool in industries ranging from healthcare and security to eCommerce and entertainment. Visual data also carries information, which speaks a thousand words. This is why the image processing concept has evolved by discovering advanced algorithms and tools.

This blog will introduce you to those common problems with image processing that can hamper information coming to you. And these are real problems that you cannot ignore if your aim is to discover what a PDF or image says.

Common Challenges with Image Processing

If you’re likely to process images, this section is for you. These insights also help address user dissatisfaction with image transformation, image enhancement, and overall image quality, which often arise when these issues are left unresolved. These quality issues are known as noise.

Noise in Images

Noise refers to any unwanted things or disturbances within a picture. Even subtle variations in pixel intensity can hide the original detail in an image. This problem with the image may occur at the time of its acquisition, transmission, or storage. Let’s check out some examples in this regard:

Medical Imaging: Have you seen MRI or CT scan images? Sometimes, they may have uncertain details, which make it difficult for a radiologist to understand and diagnose conditions accurately.
Surveillance Footage: Another problem is associated with low-light conditions. The footage may become defective, losing clarity of critical details like faces.

Types of Noise

Gaussian Noise: This type of noise can occur due to poor lighting conditions.
Salt-and-Pepper Noise: This distraction typically appeared as randomly distributed white and black pixels. Its obvious reason is erroneous transmission.
Speckle Noise: This is a very granular pattern that appears due to coherent light interactions with a rough surface. This problem may severely affect the contrast and resolution, which can be the case with ultrasound images. It leads to an unsolved diagnostic purpose.

Solution

A study by IEEE Transactions on Image Processing reveals that minimizing noise can improve the accuracy rate of further classification by up to 15%. The aforementioned cases of noise can be addressed by adding filters or employing AI algorithms. Considering filters, you may try median filters for salt-and-pepper noise data processing. On the flip side, Wiener filters can resolve the problem of Gaussian noise. For comprehensive solutions across all noise types, AI-powered tools emerge as a big change-maker.

Image Blurring

Technically, an image looks blurry when its edges are not sharply visible and its details are indistinct. Mostly, a little shake or motion during image capture becomes its obvious reason. And sometimes, it can be out-of-focus lenses and poor resolution that make an image come out blurred. Technically, an image looks blurry when its edges are not sharply visible and its details are indistinct. Mostly, a little shake or motion during image capture becomes its obvious reason. And sometimes, it can be out-of-focus lenses and poor resolution that make an image blurry.

Photography: During a photoshoot, images taken with a shaky hand can automatically lead to poor focus and, hence, blurry images.
Satellite Imaging: Another example is the picture taken by a satellite. It may appear foggy because of the satellite in motion.

Types of Blur

Motion Blur: This is obviously a challenge when you click and your hand or camera shakes a bit. It clearly causes blurriness.
Out-of-Focus Blur: When your focus settings are improper, the lens will capture blurry images.

Solution

However, it is not possible to use deblurring algorithms like Wiener deconvolution or blind deconvolution, which are popular artificial intelligence techniques, to combat this issue during image processing. But some advanced AI tools can be leveraged to foresee and restore blurred areas in images. Google AI (often a source of such advancements) is its biggest example, which can reconstruct fine details even if the resolution was low.

Distortion

Distortion refers to geometric changes that make images twisted, stretched, compressed, or bent. It can be a capturing mistake, an imperfect lens, or the viewing angle.

Document Scanning: Sometimes, scanning errors cause curved or folded edges. It makes text or images appear irregular in shape or distorted.

Types of Distortion

Barrel Distortion: This can be a result of wide-angle lenses, where lines bulge outward.
Pincushion Distortion: When the curved line bends inward, it causes distorted images.
Perspective Distortion: The tilted camera at an angle also causes distortion.

Solution

Certain image processing techniques, called rectification or warping, can eliminate distorted images. Camera calibration is another perfect solution, which employs algorithms to fix distortions that are caused by lens imperfections.

Poor Contrast

Have you seen any photocopy of a document? Sometimes, the brightness between objects and their backgrounds, or contrast, can be poor. So, it becomes difficult to differentiate text or any object in an image, especially when the light is dim or low.

Medical Imaging: Consider the case of an X-ray; its poor contrast can never make fractures and abnormalities visible.
Security and Surveillance: Another example is the nighttime security footage, which often looks dark because of low contrast in the background. It benefits the intruder and challenges surveillance agents in identifying him.

Solution

Techniques like histogram equalization and adaptive contrast enhancement are widespread to enhance contrast. Satellite images make geographic locations more noticeable using this technique.

High Computational Cost

Working with high-definition images can be computationally intensive. Think of a bulk of PDFs. They may have large datasets in high resolution. Processing these images can be a lengthy procedure.

Autonomous Vehicles: Have you seen a driverless car? It runs on real-time image processing, which critically detects objects and navigates. But high computational demands can take minutes to hours, which eventually delays decision-making.

Solution

Optimized Algorithms: Fortunately, some algorithms, like Fast Fourier Transform (FFT), have evolved to make this type of computation smoother and faster.
Hardware Acceleration: Some more evolutions, like graphics processing units (GPUs) and tensor processing units (TPUs), can be employed to reduce image processing time.
Cloud Computing: Cloud-based platforms like AWS can be embraced for faster image processing.

Real-World Example

The task of content moderation on Facebook can be challenging. But AI makes it super smooth and fast. Its AI uses distributed computing and optimization techniques to reduce processing time exponentially without compromising accuracy.

Though processing images is beneficial, it may adversely affect a business. Here is how.

Disadvantages of Image Processing

1. High Computational Cost

It's not feasible to bear an overwhelming cost associated with the processing of high-resolution images or large datasets. The integration cost of powerful hardware (GPUs/TPUs) and algorithm optimization is very high, which increases overall operational costs.

2. Data Privacy Concerns

Some images are sensitive, such as medical scans or surveillance footage. Their accuracy and privacy should be maintained to prevent the breaches of data protection laws and compliance.

3. User Dissatisfaction with Image Quality

Users may be dissatisfied with image transformation, enhancement, or overall quality, witnessing the failure of enhancement algorithms.

4. Storage and Bandwidth Requirements

As image files, especially of a high quality, occupy massive storage. It increases bandwidth, which often strains IT resources for processing files.

5. Complexity and Skill Requirement

The payscale of AI, machine learning, and algorithm experts is relatively higher, and sometimes, it can be unbearable for small businesses. These problems make advanced image processing complex.

6. Risk of Misinterpretation

Sometimes, smart tools may overly enhance or improperly filter the image, which compromises the information in the documented image. Its analysis leads to wrong or impractical solutions.

7. Processing Delay in Real-Time Applications

Delays can be commonly observed due to lagged image processing, which impacts the overall performance of real-time systems, such as autonomous vehicles. Remember, challenges are real when you practically follow these steps. To navigate them, you need a professional image processing company that can guide you through all risk factors.

Conclusion

Image processing is a globally accepted concept that inherently involves challenges related to noise, blurriness, distortion, poor contrast, and high computational cost. By strategically leveraging advanced algorithms, machine learning (AI), and specialized hardware technologies, these common challenges can be effectively addressed, leading to clearer, more accurate, and more efficiently processed visual data.

At times, disadvantages of digital image processing were in the limelight. It was the case when users used to be dissatisfied with image transformation or enhancement. But now, the latest technologies have completely changed opinions.