An automatic contrast enhancement method based on stochastic resonance

Abstract

Visual quality enhancement plays a vital role in low cost imaging systems, machine vision, industrial applications, remote sensing, face recognition systems and medical image interpretation etc. Growth of low cost image processing applications require image preprocessing which enhances details of an image. Most of the contrast enhancement papers apply desired contrast enhancement technique directly to enhance the given input image having poor contrast or contrast at any other undesired level. It is important to predict whether the contrast enhancement is needed for an image, to avoid the artifacts due to enhancement on the good image. In this paper, an algorithm to model images using its local contrast measure has been proposed, to classify and distinguish between the images having different contrast level. The input image is classified either as low contrast or high contrast image using the model. If the classified image is low contrast it will be enhanced using the Stochastic Resonance principle. The results show that the proposed automated procedure enhances the low contrast image better than the conventional enhancement methods.

Binary image enhancement based on aperiodic stochastic resonance

“…By substituting (17) into (10), the PSNR expression of the proposed image enhancement system can be calculated. In the following part, we present the computer simulations from two aspects: visual effect and PSNR performance.…”

“…Image processing is another potential application field for SR technique, and there are some initial researches on this topic [15][16][17][18][19][20][21][22][23][24][25]. Jha and Chouhan [15] and Chouhan et al [16] has introduced the SR technique into contrast enhancement, in which the classified low contrast image can be enhanced by the threshold SR, that is, the signal whose amplitude is less than the threshold can breakthrough the threshold by the assistance of the noise.…”

Dynamical stochastic resonance for non‐uniform illumination image enhancement

“…Rallabandi et al used SR to lessen the inherent noise and enhance the magnetic resonance images by adding noise in Fourier space [7]. Maragatham et al exploited SR to enhance the low‐contrast images by adding Gaussian noise to the dark regions [8]. All the results show that the appropriate amount of noise is helpful to signal enhancement.…”

Guided Image Filter and Adaptive Histogram Specification based DBLA for Enhancing Remotely Sensed Images

“…Ultimately, the particular service vector unit (SVM) classifier must be used to help classify picture clips. G. Maragatham et al [15] recommended an algorithm to help product photographs displaying its local distinction gauge, to help classify and also recognize associated with the particular photographs possessing unique distinction degree. The insight picture is actually grouped either because lower distinction or excessive distinction picture by way of the particular product.…”