A restoration model considering the data-dependent multiplicative noise, shift-invariant blur, and haze has been introduced in this paper. The proposed strategy adopts a two-step model to perform a single image dehazing under the blurred and noisy observations. The first step uses the well-known dark channel prior method to estimate the transmission of the medium and atmospheric light that signifies the global color of the haze and dehaze the images. The second step performs denoising and deblurring under a Gamma distributed noise setup and a linear blurring artefact. The restoration under the above mentioned setup has quite a few applications in satellite and long-distant telescopic imaging systems, where the captured images are noisy due to atmospheric pressure turbulence and hazy due to the presence of atmospheric dust formation; further they are blurred due to the common device artefacts. The proposed strategy is tested using a large amount of available image-sets and the performance of the model is analysed in detail in the results section. 1. Introduction Image dehazing is a common inverse problem in the image processing literature. The light received by a sensor from outdoor scenes is often absorbed and scattered by the medium through which the light ray travels; the common examples would be dust, mist, fog, fumes, and so forth. Therefore, the captured outdoor images are commonly found to be degraded with fog, mist, or haze. The visibility of the scene contents would be highly challenged in such images. Nevertheless, most of the outdoor imaging systems like surveillance and transport navigation should extract semantic information for proper analysis. Therefore, dehazing becomes an inevitable part of such image restorations. Moreover in long distance photography of foggy scenes, this process has a substantial effect on the image in which contrasts are reduced and surface colors become faint. In addition to the dense haze formation in captured data, sometimes the variations in atmospheric pressure conditions or transmission errors can cause introduction of noise in images. Such images are noisy and hazy and the restoration involves a denoising and dehazing process. Moreover, the assumption that the imaging systems are not free from the devise artifacts leads to the necessity of a deblurring process along with denoising and dehazing. In this work we assume a data-correlated noise and spatially invariant out-of-focus blur. The noise intensity distribution is assumed to be Gamma and multiplicative in nature. The blurring is devised as a linear and
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