Image Restoration by Matching Gradient Distributions

Abstract

Abstract-The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. We present an alternative deconvolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that a reconstructed image should have a gradient distribution similar to a reference distribution. In natural images, a reference distribution not only varies from one image to another, but also within an image depending on texture. We estimate a reference distribution directly from an input image for each texture segment. Our algorithm is able to restore rich mid-frequency textures. A large-scale user study supports the conclusion that our algorithm improves the visual realism of reconstructed images compared to those of MAP estimators.

Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior

“…A highly sparse (concave) prior can ultimately be more effective in differentiating sharp images and fine structures than a convex one. Detailed supported evidence for this claim can be found in [1], [29], [30], [31]. However, if such a prior is applied at the initial stages of estimation, the iterations are likely to become trapped at suboptimal local minima, of which there will always be a combinatorial number.…”

Digital Image Denoising Using Histogram and Dynamic Filters

“…Based on the type of image and type of noise with which it is corrupted, a slight change in individual method or combination of any methods further improves visual quality. In this survey, we focus on survey the existing techniques of image enhancement, which can be classified into two broad categories as spatial domain enhancement and Frequency domain based enhancement [2]. N.Mohanapriya and B. Kalaavati presented spatial domain Denoising techniques along with their algorithm and also analyzes their performance based on the image quality for medical images [3].…”

Performance Analysis and Optimization of Linear Restoration in Spatial Domain

“…In the image restoration literature degradation often assumed to be an AWGN. A widely used estimation techniques are based on the mean absolute deviation [14].In [15] quantitative measures are estimated for each intensity in spatial domain. A Stefano and P. Whites, system was based training samples in other domain only for natural images [16].Generalization expectation maximization restoration techniques in any domain developed and estimate the spectral features.…”