This is where deep learning comes into the picture and experiments have proved that training a convolutional blind denoising deep learning network outperforms other conventional image denoising techniques by a large margin. So, to tackle this issue of denoising real-world noisy images, there is a need of using more advanced techniques. Due to this, most of the denoising techniques performed poorly in removing real noise from images. The real-world noise (also known as blind noise) is more sophisticated and diverse. It usually occurs as white and black pixels in the image.īut this assumption doesn’t completely hold for real noise in photographs. Impulse Noise - caused by sharp and sudden disturbances in the image signal. the pixel values that these noises can take are Gaussian distributed.
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Gaussian Noise - Noise having PDF equal to the normal distribution. Most of these techniques assumed these noises in images to be Gaussian noise or impulse noise. Over the years many techniques and ideas have been introduced for image denoising. The task of image denoising has been an interesting area of research for decades. Therefore, the task at hand is to develop a solution that would remove these noises from images thereby improving image quality and retaining relevant pieces of information in the image. The process of removing such noises from images is known as Image Denoising. So, there is a need to remove these noises from images when it comes to low-level vision tasks and image processing. This can degrade the captured image's quality and can cause loss of information as the original pixel values are replaced by random values due to noise. These noises can appear due to many reasons such as electric signal instabilities, malfunctioning of camera sensors, poor lighting conditions, errors in data transmission over long distances, etc. The images that are captured in the real world come with noises.
Experimenting with Deep Learning models.
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