Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction

In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-exposure image fusion. To bring out more well-exposed contents, we generate a more helpful intermediate virtual image for fusion using the proposed Optimized Adaptive Gamma Correction (OAGC) to have better contrast, saturation, and well-exposedness. Fusing the input images with the enhanced virtual image works well even though both inputs are underexposed or overexposed, which other state-of-the-art fusion methods could not handle. The experimental results show that our method performs favorably against other state-of-the-art image fusion methods in generating high-quality fusion results.

Research on Image Enhancement Algorithm Based on Artificial Intelligence

With the continuous development of social science and technology, people have higher and higher requirements for image quality. This paper integrates artificial intelligence technology and proposes a low-illuminance panoramic image enhancement algorithm based on simulated multi-exposure fusion. First, the image information content is used as a metric to estimate the optimal exposure rate, and the brightness mapping function is used to enhance the V component, and the low-illuminance. The image and the overexposed image are input, the medium exposure image is synthesized by the exposure interpolation method, and the low illumination image, the medium exposure image and the overexposure image are merged using a multi-scale fusion strategy to obtain the fused image, which is corrected by a multi-scale detail enhancement algorithm. After the fusion, the details are enhanced to obtain the final enhanced image. Practice has proved that the algorithm can effectively improve the image quality.

Smoke Removal and Image Enhancement of Laparoscopic Images by An Artificial Multi-Exposure Image Fusion Method

In laparoscopic surgery, image quality is often degraded by surgical smoke or by side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The degraded images complicate the work of the surgeons and may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global image contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing the Multi-Exposure Fusion (PASD-MEF) technique to enhance the local contrast of laparoscopic images for better visualization. The set of under-exposure level images are obtained from a single input blurred image by using gamma correction. Spatial linear saturation is applied to enhance image contrast and to adjust the image saturation. The Multi-Exposure Fusion (MEF) is used on a series of multi-exposure images to obtain a single clear and smoke-free fused image. MEF is applied by using adaptive structure decomposition on all image patches. Image entropy based on the texture energy is used to calculate image energy strength. The texture entropy energy determined the patch size that is useful in the decomposition of image structure. The proposed method effectively eliminate smoke and enhance the degraded laparoscopic images. The qualitative results showed that the visual quality of the resultant images is improved and smoke-free. Furthermore, the quantitative scores computed of the metrics: FADE, Blur, JNBM, and Edge Intensity are significantly improved as compared to other existing methods.

DCT based Fusion of Variable Exposure Images for HDRI

Combining images with different exposure settings are of prime importance in the field of computational photography. Both transform domain approach and filtering based approaches are possible for fusing multiple exposure images, to obtain the well-exposed image. We propose a Discrete Cosine Trans- form (DCT-based) approach for fusing multiple exposure images. The input image stack is processed in the transform domain by an averaging operation and the inverse transform is performed on the averaged image obtained to generate the fusion of multiple exposure image. The experimental observation leads us to the conjecture that the obtained DCT coefficients are indicators of parameters to measure well-exposedness, contrast and saturation as specified in the traditional exposure fusion based approach and the averaging performed indicates equal weights assigned to the DCT coefficients in this non- parametric and non pyramidal approach to fuse the multiple exposure stack.

Contrast and Synthetic Multiexposure Fusion for Image Enhancement

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

Curvature and Entropy Statistics-Based Blind Multi-Exposure Fusion Image Quality Assessment

The multi-exposure fusion (MEF) technique provides humans a new opportunity for natural scene representation, and the related quality assessment issues are urgent to be considered for validating the effectiveness of these techniques. In this paper, a curvature and entropy statistics-based blind MEF image quality assessment (CE-BMIQA) method is proposed to perceive the quality degradation objectively. The transformation process from multiple images with different exposure levels to the final MEF image leads to the loss of structure and detail information, so that the related curvature statistics features and entropy statistics features are utilized to portray the above distortion presentation. The former features are extracted from the histogram statistics of surface type map calculated by mean curvature and Gaussian curvature of MEF image. Moreover, contrast energy weighting is attached to consider the contrast variation of the MEF image. The latter features refer to spatial entropy and spectral entropy. All extracted features based on a multi-scale scheme are aggregated by training the quality regression model via random forest. Since the MEF image and its feature representation are spatially symmetric in physics, the final prediction quality is symmetric to and representative of the image distortion. Experimental results on a public MEF image database demonstrate that the proposed CE-BMIQA method achieves more outstanding performance than the state-of-the-art blind image quality assessment ones.