Rendering technology is used in many industries

including home decoration

to improve the visual effects of images or videos. Designers use this technology to produce real and attractive designs. The quality of a rendered image of home decoration design relies on the quality of design parameters

such as sharpness or colorfulness

which depend on the renderer. The rendered images usually have few problems with sharpness because they are always partially gray due to the fixed parameters of the renderer and the multiple parameters of complex light. As such

manual optimization is necessary; this process requires a considerable amount of time and energy of the designer and cannot avoid subjectivity. Few enhancement methods have been developed to enhance the quality of rendered images but cannot yield acceptable results. In this paper

an adaptive enhancement method is proposed to deal with the rendered home decoration design images; the method combines three image enhancement elements

including brightness

contrast

and saturation by the neural networks. The proposed method combines different algorithms to improve the three elements of image enhancement and uses the neural networks to learn the subjective parameters of the rendered images. First

the image enhancement method based on the saturation of an original image is used to enhance the brightness and contrast of the image

wherein the saturation component of the color image is computed in the HSI color space. Two different exposed images are generated using weighting function method. The enhanced image is obtained by fusing the original image and the two exposed images. However

contrast enhancement is still insufficiently strong because the algorithm is mainly aimed at enhancing the brightness. Therefore

the histogram equalization is added in to further enhance the contrast because it is simple to understand and calculate. In many conditions

histogram equalization will not produce ideal results due to the noises in images

while this problem is disregarded in the rendered images. In consideration of the similarity between brightness and contrast

the two algorithms are fused with two enhancement factors. Finally

a color matrix is used to enhance the saturation in the RGB color space. The saturation enhancement is also provided as an enhancement factor to be fused with the brightness and the contrast because of the connections among them. The nonlinear mapping relation between the mean and variance of the brightness

the mean and variance of saturation of the original image and the enhancement factors of brightness

contrast

and saturation is established based on the neural networks. Conventional image enhancement methods cannot acceptably and automatically enhance the rendered images because the features of the rendered images are special and the enhancement needs to meet human visual system(HVS) as well. In the proposed method

the enhancement factors are automatically determined according to the neural networks established on three different algorithms to realize the adaptive enhancement of the rendered images. The effectiveness of the proposed algorithm is verified on few rendered home decoration design images that are partially gray in different degrees. These experimental images are all designed by the same designer in case of few unnecessary errors. The proposed method is also compared with few classical image enhancement algorithms by the histogram

information entropy

average contrast(AC)

and average gray(AG). Experimental results present that the histograms of the processed images have very few information loss and maintain features very well. In addition

information entropy

AC

and AG of the processed images considerably increased compared to those of the original images. Compared to the other methods

the proposed method can achieve a higher increase in degree on these quantitative evaluations. Experimental results show that the proposed method can effectively enhance the rendered images adaptively. Experimental results show that the proposed method can properly enhance the brightness

contrast

and saturation of the rendered images with different degrees

which proves its suitability for the enhancement of partially gray rendered images. Furthermore

the proposed method is also easy and fast to compute. However

we cannot deny that this method has few limitations on the conditions wherein the materials are more reflective than normal situations. In addition

the sunlight is simultaneously very strong

resulting in extremely colorful and unreal reflective materials. Hence

the method should be further studied to adapt to few extreme situations.