Visualization of inner gastrointestinal (GI) tract is an important aspect in diagnosis of diseases such as the bleeding and colon cancer. Wireless capsule endoscopy (WCE) provides painless imaging of the GI tract without much discomfort to patients via near-lights imaging model and with burst light emitting diodes (LEDs). This imaging system is designed to minimize battery power and the capsule moves through the GI tract with natural peristalsis movement and the color video data are captured via wireless transmitter in the WCE. Despite the advantages of WCE videos, the obtained frames exhibit uneven illumination and sometimes result in darker regions that may require enhancement afterwards for better visualization of regions of interest. In this work, we extend a human visual system (HVS) based image enhancement model that uses a feature-linking neural network model based on timing precisely of the spiking neurons. Experimental results on various WCE frames show that we can obtain better enhancement of regions of interest and compared to other enhancement approaches in the literature we obtain better quality restorations in general. Further, we show the applicability of our enhancement method on improving the automatic image segmentation, and 3D shape from shading visualization reconstruction indicating that it is viable to be used within a computer-aided diagnosis systems for GI tract diseases.